BiC [1]}, which uses an additional layer to correct task bias in the model and train the model in two stages. LUCIT [2]} uses a cosine normalization layer instead of the standard softmax layer and proposes to use margin ranking loss to prevent inter-task confusion. Finally, IL2M [3]} uses statistics of predictions of classes from previous tasks.
